Module Review

This chapter looked at FME Performance and some of the techniques available to improve it

What You Should Have Learned from this Module

The following are key points to be learned from this session:

Feature Caching enables inspection of the data at any point in the translation
Bookmarks are good for planning a workspace and can be used to increase performance when caching features
Partial runs allow you to run sections of workspaces without running the entire thing.
Analyzing a log file helps to determine where performance improvements can be made
Performance is the measure of useful work done in a given time. Excess data and caching of data to disk are two factors that can impact performance greatly
Improve reading performance by reducing the amount of data being read
Improve writing performance by ordering the FME Writers correctly
Improve transformation performance by removing excess attributes and properly managing group-based transformers
Upload larger tasks to multiple engines on FME Server
Make use of all reader/writer parameters to improve database performance
Employ parallel processing to make FME multi-threaded
Use batch processing to use multiple FME engines with multiple datasets

The ability to design a workspace before starting
The ability to analyze and deconstruct an FME log file
The ability to remove no data from rasters
An understanding of potential methods for improving reader, writer, and transformer performance
The ability to use database parameters to improve performance
The ability to apply parallel processing in an FME workspace